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About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role You'll partner with Developer Productivity engineering leadership to define what "developer productivity" means in an AI-first org and to set the strategy for how Anthropic measures, understands, and improves it. This is a space where the playbook doesn't exist yet: AI-assisted development is reshaping how engineers work faster than anyone can measure, and last quarter's answer is already suspect. You'll decide which questions are worth asking, build the evidence to answer them, and stay ready to revise when the ground shifts again. You'll own the data strategy end-to-end: which metrics earn the org's trust, which investments to push for, which assumptions to challenge — including your own. The space rewards people who hold conclusions loosely, instrument early, and update fast when the data disagrees with the narrative. This role sits at the intersection of data science, developer experience, and frontier AI, with Anthropic's own teams as your users. Key responsibilities Lead ambiguous, high-stakes investigations where the question isn't yet well-formed — from "is Claude making engineers faster?" to "what does 'faster' even mean here?" Treat findings as provisional in a space that changes month to month. Bias toward instrumenting first, collecting evidence broadly, and revising the team's priors as the picture sharpens Partner with Developer Productivity engineering leadership to set the team's measurement and research agenda — what to study, what to build, what to stop Define the metrics framework for developer productivity in an AI-augmented org, and drive its adoption as the basis for tooling and infrastructure investment decisions Design and run experiments on internal tooling and workflow changes; build the causal evidence base for what actually moves productivity Influence engineering, infrastructure, and product leadership with data. Push back when the data doesn't support the prevailing narrative, and say so plainly when it doesn't support yours either Build the analytical foundations (pipelines, dashboards, models) yourself or through partners — staying hands-on and close to the work rather than directing from a distance Minimum qualifications Experience writing production-quality SQL and Python (or a similar language) to build pipelines, dashboards, and models independently Experience serving as the primary data or analytics voice in a space where the questions weren't yet well-defined, and helping define them A track record of holding conclusions loosely — favoring instrumentation and evidence-gathering over defending a prior position, and revising views in public when the evidence warrants it Experience shaping what an engineering or product team worked on, not only measuring what they shipped — being consulted before a decision was made, not just after Genuine interest in how AI is changing the way software gets built, with some firsthand experience grappling with the harder, less-defined parts of that question Comfort presenting data-backed conclusions to a room of engineers, including when that means saying a built feature isn't moving the needle Preferred qualifications 8+ years of hands-on data science experience, ideally in infrastructure, performance, or platform contexts Direct experience with developer productivity, developer experience, or internal tooling, at any scale Experience measuring the adoption or impact of AI-assisted workflows, or other tooling where the ground truth was contested A track record of building an experimentation or causal-inference practice in an org that didn't already have one Prior staff-level or tech-lead scope: setting direction for other ICs and owning a domain's data strategy end to end Deadline to apply: None. Applications are reviewed on a rolling basis. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $380,000 — $460,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run. Responsibilities Build and improve the RL training infrastructure that researchers depend on day-to-day Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster Own the reliability and performance of research runs end-to-end Contribute to design decisions that shape how Anthropic does RL at scale You may be a good fit if you Have strong software engineering fundamentals and a track record of building performant, reliable systems Have worked on ML infrastructure, distributed systems, or research tooling Care about enabling other people's work and find leverage through platforms rather than individual experiments Are comfortable operating across the stack, from low-level performance work to RL algorithms Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego Strong candidates may also have Experience with large-scale distributed training (RL, pre-training, or post-training) Familiarity with JAX, PyTorch, or similar ML frameworks A track record of operating at the edge of research and infra in a fast-moving environment Deadline to apply: None. Applications will be reviewed on a rolling basis. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 — $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role We're looking for Research Engineers to build the evaluations that tell us — and the world — what Claude can actually do. Your work will turn ambiguous notions of "intelligence" into clear, defensible metrics that researchers, leadership, and the public can rely on. You'll design and implement evaluations across the full spectrum of Claude's capabilities and personality, and build the infrastructure that runs them reliably at scale. You'll partner closely with researchers throughout the lifecycle of a new capability — from defining what to measure, to running the eval against live training checkpoints, to interpreting the results. The goal is to make Anthropic the leader in extremely well-characterized AI systems, with performance that is exhaustively measured and validated across the tasks that matter. Key responsibilities Design and run new evaluations of Claude's capabilities — reasoning, agentic behavior, knowledge, safety properties — and produce visualizations that make the results legible to researchers and decision-makers Build and harden the distributed eval execution platform so hundreds of evals run reliably against checkpoints throughout production RL training runs Own the dashboards researchers and leadership use to monitor model health during training, improving signal-to-noise, reducing latency, and making regressions impossible to miss Debug anomalous eval results mid-training-run, determine whether the cause is a model change or an infrastructure issue, and communicate the answer clearly under time pressure Improve the tooling, libraries, and workflows researchers use to implement and iterate on evaluations Partner with research teams across the full lifecycle of a new capability — from defining what to measure to interpreting results as training progresses Run experiments to characterize how prompting, sampling, and scaffolding choices affect results on internal and industry benchmarks Communicate evaluations and their results to internal stakeholders and, where appropriate, external audiences Minimum qualifications Strong Python programming skills, including production or research infrastructure Experience building or operating distributed systems, data pipelines, or other infrastructure that needs to be reliable at scale Clear written and verbal communication, especially when explaining technical results to non-specialists Comfort operating in an on-call or production-support capacity when training runs are live Care about the societal impacts of your work and an interest in steering powerful AI to be safe and beneficial Preferred qualifications Hands-on experience using large language models such as Claude, including prompting, sampling, and scaffolding Background in data visualization and a track record of building dashboards people actually trust and use Experience developing robust evaluation metrics for language models Experience with observability, monitoring, or experiment-tracking systems Background in statistics and experimental design Experience with large-scale dataset sourcing, curation, and processing Experience running or supporting ML training infrastructure A bias toward picking up slack and operating flexibly across team boundaries Enjoy pair programming — we love to pair Representative projects Stand up a new eval that tests a specific reasoning capability from scratch — define the task, build the dataset, implement the scoring, validate against known signals, and ship a dashboard that makes the result legible Diagnose a mid-training regression: an eval suite returns anomalous numbers, and you need to determine within hours whether it's the model, the harness, the data, or the infrastructure Take a flaky distributed eval pipeline and make it boring — better retries, better observability, faster feedback to researchers Partner with a research team on a new capability area, helping them articulate what "good" looks like and translating that into measurable artifacts The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 — $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic's Human Data Platform team builds systems designed to collect data that improves our models. This includes the infrastructure to simulate real-world environments and tasks, novel interfaces for data vendors to use, and the pipelines that enable researchers to gather high-quality data at scale. As Claude's real-world usage evolves, so do our data needs — and our tooling has to keep pace. You'll work alongside an engineering team that's quickly prototyping and shipping, help make smart bets about where to focus, and ensure we're investing in tooling that scales. You'll work across research teams, data ops, and external vendors, translating what you learn into clear direction on what to build next. Responsibilities Own the product direction for our human data tooling, with clear prioritization across labeling interfaces, infrastructure investments, data quality, and operational visibility Partner with engineering to scope and ship quickly, staying close to the work in a fast-moving prototyping environment Develop a deep understanding of research and training approaches to identify where tooling investments will have the highest leverage Identify patterns across one-off requests and push toward reusable infrastructure that compounds over time Sit in on crowd worker and vendor sessions to systematically understand pain points Define and track outcome-based KPIs: time-to-launch for new data collection projects, end-to-end data quality scores, and measurable impact on model evaluation scores Minimum Qualifications You May Be a Good Fit If You Believe that advanced AI systems could have a transformative effect on the world and are interested in helping make sure that transformation goes well Are drawn to ambiguous, high-stakes environments where you’ll play a big role in defining the product strategy Shipped products where they had to deeply understand technical constraints, not just translate requirements Experience working directly with research teams, ideally in AI/ML contexts Are equally comfortable talking to crowdworkers about their workflow and to research teams about data quality methodology Are a quick study—this team sits at the intersection of a large number of different complex technical systems that you'll need to understand (at a high level) to be effective Have an interest in how humans interact with AI systems and how to design experiences that elicit high-quality data Preferred Qualifications Strong Candidates May Also Have Experience building data collection tools, annotation platforms, or human-in-the-loop pipelines Experience working with researchers who are internal users/customers Good instincts and an eye for intuitive user experiences, particularly those involving complex UI interactions or annotation workflows Strong project management skills: prioritization, communicating across team/org boundaries The ideal candidate has the following virtues/values: Have 5+ years in product management, with experience launching new products and scaling existing products. Intellectual curiosity without ego: Comfortable not knowing things and asking questions to learn Autonomous learning: Able to independently figure out how systems work and develop expertise quickly Researcher EQ: Deep understanding of how researchers think and what motivates them Product creativity: Ability to see novel product opportunities emerging from research capabilities Founder mentality: Track record of doing whatever it takes to ship highly technical products The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $305,000 — $385,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: As an Applied AI team member at Anthropic, you will be a Pre-Sales architect focused on becoming a trusted technical advisor helping customers understand the value of Claude and paint the vision on how they can successfully integrate and deploy Claude into their technology stack. You'll combine your technical depth with customer-facing skills to architect innovative LLM solutions that address complex business challenges while maintaining our high standards for safety and reliability. As a Commercial Solutions Architect, you'll go deep with priority accounts as a hands-on builder, while creating reusable blueprints, demos, and enablement that extend Claude's reach across the broader Commercial book of business. Working closely with our Sales, Product, and Engineering teams, you'll guide customers from initial technical discovery through successful deployment. You'll leverage your expertise to help customers understand Claude's capabilities, develop evals, and design scalable architectures that maximize the value of our AI systems. Responsibilities: Partner with account executives to deeply understand customer requirements and translate them into technical solutions, ensuring alignment between business objectives and technical implementation Serve as the primary technical advisor to customers throughout their Claude adoption journey, from discovery to initial evaluation through deployment. You will need to coordinate internally across multiple teams and stakeholders to drive customer success Support customers building with the Claude API, Claude Code, and Claude for Enterprise Ship working code. Build prototypes and proof-of-concepts hands-on, develop eval frameworks, and write near-production examples that customers can extend Build reusable blueprints, demos, and enablement assets that scale across customers Guide technical architecture decisions and help customers integrate Claude effectively into their existing technology stack Help customers develop evaluation frameworks to measure Claude's performance for their specific use cases Identify common integration patterns and contribute insights back to our Product and Engineering teams Travel occasionally to customer sites for workshops, technical deep dives, and relationship building Maintain strong knowledge of the latest developments in LLM capabilities and implementation patterns You may be a good fit if you have: 3+ years of highly technical experience as a software engineer (or equivalent) with some customer-facing exposure, OR 3+ years as a Solutions Architect, Sales Engineer, or Technical Account Manager with strong hands-on building experience A builder identity. You've shipped real software, you have technical taste, and you care about the craft of what you build A systems mindset. When you see a problem, your instinct is to ask "how do I make this reusable." You'd rather build one thing that serves ten customers than ten things that serve one each Strong coding ability. You ship prototypes regularly and can work in a real codebase, not just notebooks. Comfort with Python expected Strong ability to build trust with technical stakeholders and adjust your communication for varied audiences Strong technical communication skills with the ability to translate customer requirements between technical and business stakeholders Experience designing scalable cloud architectures and integrating with enterprise systems Familiarity with common LLM frameworks and tools, or a background in machine learning or data science Comfort operating in early-stage, ambiguous environments where the playbook doesn't exist yet, and a track record of building structure as you go Excitement for engaging in cross-organizational collaboration, working through trade-offs, and balancing competing priorities A love of teaching, mentoring, and helping others succeed Passion for thinking creatively about how to use technology in a way that is safe and beneficial, and ultimately furthers the goal of advancing safe AI systems The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $240,000 — $315,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role As part of our growing Data Science & Analytics team, you will play an instrumental role in Anthropic's mission of building safe and beneficial AI — this time by driving data-informed decisions across the commercial customer lifecycle. This role sits at the intersection of fast-moving sales operations and rigorous statistical analysis. You will work across multiple segments and products, partnering with analytics engineers, fellow data scientists, and go-to-market leadership to turn complex commercial data into actionable strategy. You will own measurement and analysis for new logo acquisition through activation, expansion, and retention for a rapidly scaling, consumption-based AI platform. You've worked in cultures of analytical rigor before, and you're eager to help shape the norms and best practices of a growing data science function at a pivotal moment in the company's growth. Key responsibilities Define key metrics, build measurement frameworks, and maintain core reporting to evaluate GTM success across segments and products Analyze commercial and user data to surface actionable insights, size opportunities, and influence roadmaps and go-to-market strategy Develop hypotheses and apply rigorous causal inference methods — controlled experiments, synthetic controls — to make clear, actionable recommendations Investigate anomalies, conduct root cause analyses, and provide data-driven guidance on priorities and decisions Build statistical models, optimization frameworks, and simulations to support and automate commercial decision-making processes Present analyses and recommendations to both technical and non-technical stakeholders, including GTM leadership Establish foundational data practices and help scale analytics infrastructure to support rapid product and commercial iteration Minimum qualifications Proficiency in Python, SQL, and data visualization tools Expertise in experimental design, causal inference, statistical modeling, and A/B testing, particularly in high-scale technical environments Demonstrated ability to translate complex data into clear, actionable insights for both technical and business audiences Strong written communication and presentation skills Ability to work effectively in fast-moving, ambiguous environments — comfortable creating structure and driving progress where neither yet exists Preferred qualifications 5+ years of experience in data science or analytics roles A strong track record in multi-segment, multi-product B2B sales or commercial analytics, especially with consumption-based revenue models Experience with AI/ML products, large language models, or developer tools in the AI/ML ecosystem Genuine interest in Anthropic's mission of developing safe and beneficial AI The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $285,000 — $380,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic is seeking a talented and driven Workday Business Systems Analyst to join our People Systems team. You'll own the technical configuration, testing, and operational excellence of our Workday HRIS across two domains: PATT and HCM. Your deepest work will be on the PATT side, where you'll refine our existing payroll, absence, and time tracking configuration and shape PATT design decisions as they're made. On the HCM side, you'll own core HCM data structures, business process frameworks, and the security model that underpins everything we do in Workday. You'll be relied on as a system expert for both strategic guidance and hands-on delivery. This role is about moving the team forward, not just keeping the system running. The right candidate is passionate about where AI and internal tooling can make People Systems more effective, and treats "the way it's always been done" as a starting point to question rather than a constraint. We're looking for someone who is naturally curious and an exceptional problem solver — the kind of person who looks beyond the obvious or "standard" Workday answer and asks why before how. You'll find success in this role if you thrive in a fast-paced, high-growth environment and know how to reprioritize without losing rigor. You'll partner across many parts of the business, from the broader People team to Finance, IT, and beyond, to make sure our HRIS is secure, scalable, and a true business enabler. Responsibilities PATT and HCM Configure and maintain Payroll: pay components, earnings and deductions, pay groups, period schedules, and payroll-related business processes Configure and maintain Absence: time-off plans, accrual rules, eligibility criteria, and leave types Configure and maintain Time Tracking: time entry templates, validations, work schedules and calendars, and overtime calculations Configure and maintain Core HCM foundations: supervisory organizations, staffing models, job profiles and job/compensation structures, custom organizations, and worker data integrity Support annual and recurring People cycles in Workday, including compensation review setup, open enrollment configuration, and org restructures (mass supervisory org changes and job catalog updates) Design, build, and maintain business process definitions in both domains, including condition rules, calculated fields, custom validations, routing and approval logic, and notifications Partner with the Payroll team to troubleshoot configuration-driven payroll issues, including gross-to-net discrepancies, retro calculations, and off-cycle errors — the Payroll team owns processing; you own the configuration behind it Bring a strong command of the Workday security model: role-based, user-based, and intersection security groups, domain security policies, business process security policies, and integration system users (ISUs), with sound judgment about when to leverage each as you configure and troubleshoot across domains Support security and data audits in partnership with the broader team, using Workday delivered and custom audit reports to analyze access patterns, remediate exceptions, and uphold segregation of duties Perform impact analysis before changes ship, including how HCM and security changes ripple into payroll, absence, and time data Lead testing and adoption for Workday's twice-yearly releases across both domains: assess new features, run regression testing on critical business processes and security, and drive rollout of features we opt into Across the platform Translate business requirements from our internal partners into scalable system design, and document configuration decisions as you go Build trusted relationships with partner teams, acting as a translator between technical configuration and business needs Support change management for system updates and new functionality: communicate what's changing and why, prepare documentation and enablement materials, and make sure changes land well with the people who use them Build and maintain advanced, matrix, and composite reports and dashboards; support mass data changes via EIB Configure alerts, scheduled reports, and report security so the right data reaches the right people automatically Triage and resolve day-to-day system issues, getting to root cause rather than patching symptoms Actively look for where AI and automation can strengthen how People Systems works, from testing, documentation, and requirements drafting to ticket triage and report generation, using Anthropic's own tools as part of your daily workflow Drive process automation and optimization opportunities with the broader People team and partner functions Minimum Qualifications Have deep hands-on working knowledge of Workday Payroll, Absence, and Time Tracking (PATT) configuration and design, business process configuration, and Security Have hands-on working knowledge of Workday Core HCM Have worked in a fast-paced, high-growth environment and know how to flex between roles Possess strong technical skills including understanding of Workday architecture, security models, and integration methodologies Have experience with full lifecycle Workday implementations and/or significant module expansions Are intellectually curious, you ask "why" before "how," explore root causes rather than symptoms, and stay current on Workday and adjacent HR tech because you want to, not because you have to Approach problems with creativity, comfortable looking past the standard or obvious solution to find an answer that actually fits the business Treat HR data security and privacy as a non-negotiable foundation in everything you build and maintain Excel at building relationships and communicating effectively with technical and non-technical stakeholders at all levels Are detail-oriented with strong documentation skills and a commitment to process excellence Have a track record of managing multiple competing priorities, with a bias toward flexibility and impact Have a collaborative mindset and willingness to pick up slack even if it goes outside your job description Preferred Qualifications Have 7+ years of experience as a Workday Business Systems Analyst, with demonstrated expertise in system configuration, testing, and implementation Workday Pro accreditations or Workday partner certifications in HCM, Payroll, Absence, Time Tracking Understanding of Workday integrations: familiarity with Workday integration tools (EIB, Core Connectors including PECI/PICOF/DCoD, Workday Studio, SOAP/REST Web Services / RaaS), inbound vs. outbound patterns, authentication (SSO, SFTP, API keys / certs), typical HR integration partners (benefits providers, payroll, ATS, IdP, background check), and disciplined error handling, reconciliation, and monitoring Exposure to Workday Prism Analytics, Extend, or advanced reporting beyond standard report writer Experience applying AI tools to HRIS or People operations work, or strong interest in building that muscle Knowledge of complementary HR/Finance systems and integration patterns Understanding of data privacy regulations and security best practices The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $270,000 — $270,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The Research Data Platform team builds the tools that Anthropic's researchers use every day to manage, query, and analyze the data that goes into training and evaluating frontier models. We power the internal applications researchers rely on to monitor RL runs, explore finetuning datasets, and understand what's happening inside their experiments. We're looking for engineers who love working directly with users and who excel at building data products — the pipelines that move data out of training runs into queryable storage, and the APIs, libraries, and services researchers use to manage and explore it. This role sits closer to the research workflow than a typical data infrastructure position: you'll often embed with research teams, build ML-specific tooling alongside them, and leverage what our Data Infrastructure team has already built rather than reinventing it. We do not require prior ML or AI training experience. If you enjoy working closely with technical users, learning new domains quickly, and building tools people actually want to use, you'll pick up the research context fast. Responsibilities Build and operate data pipelines that extract data from research training runs and land it in storage systems that are easy and fast to query Work closely with researchers to design and build APIs, libraries, and web interfaces that support data management, exploration, and analysis Develop dataset management, data cataloging, and provenance tooling that researchers use in their day-to-day work Embed with research teams to understand their workflows, identify high-leverage tooling opportunities, and ship solutions quickly Collaborate with adjacent teams to build on existing systems rather than reinventing them You may be a good fit if you Have significant software engineering experience, particularly building data-intensive applications or internal tooling Enjoy working directly with users, gathering requirements iteratively, and shipping things that get adopted Are results-oriented, with a bias towards flexibility and impact Pick up slack, even if it goes outside your job description Want to learn more about machine learning research Care about the societal impacts of your work Strong candidates may also have experience with Large-scale ETL, columnar storage formats, and query engines (e.g., Spark, BigQuery, DuckDB, Parquet) High-volume time series data — ingestion, storage, and efficient querying Data cataloging, lineage, or metadata management systems ML experiment tracking or metrics platforms Working in environments where engineers partner closely with quantitative users — research labs, trading firms, observability or analytics startups Complex data visualization and full-stack web application development The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $405,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic's researchers generate and depend on enormous amounts of data — training runs, evaluations, RL transcripts, annotations etc... The Research Data Platform team builds the systems that make that data easy to produce, find, query, and trust. We work in two modes: we build platform components that other systems plug into (for example, a metrics library that training frameworks integrate to record and retrieve run data), and we own core datasets end to end (for example, the data pipeline behind RL transcripts). As the team's tech lead, your job starts with our users. You'll work directly with researchers — and with the engineers who support them — to understand how they actually work, where managing data slows them down, and where a well-built platform component or a well-curated dataset would change what's possible. You'll turn what you learn into technical direction for the team, in partnership with the team's manager, who owns priorities and people. A central ambition you'll drive: a small set of canonical, well-documented datasets — starting with the core data model for RL — that researchers trust and standardize on, rather than every team managing its own copies. You'll spend your first few months close to the code and close to users: shipping improvements in our core systems, embedding with research teams, and building your own map of their workflows. As the team grows, this role has a natural path into formal people leadership for someone who wants it. Responsibilities Work directly with researchers and the engineers supporting them to understand their workflows, identify the highest-leverage opportunities, and shape what the team builds next Set the technical direction for the team across our platform and our datasets Design and build platform components that other teams plug into — libraries, services, and interfaces such as the metrics library used by training frameworks Own core datasets end to end: the pipelines that produce them, the schemas that define them, and the documentation and guarantees that make researchers trust them Drive convergence toward canonical datasets — including the core data model for RL transcripts — that research teams standardize on Lead complex, multi-quarter projects that span several systems and teams, staying hands-on in the code Raise the team's technical bar through design reviews, mentorship, and the quality of your own work You may be a good fit if you: Have built and operated data-intensive systems at scale — pipelines, storage layers, query systems — with strong instincts for data modeling and schema design that hold up as usage grows Have set technical direction for a team, or owned the architecture of a data platform that other teams build on Treat internal users as customers: you do the discovery work, iterate with users, and measure success by adoption rather than by shipping Understand that researchers aren’t typical internal customers — the work is exploratory by nature, workflows differ from team to team, and requirements are discovered through experiments rather than specified up front Can build for that motion — keeping interfaces stable and data trustworthy while use cases change underneath you, and judging when a quick, disposable solution serves research better than a durable one Lead through influence — aligning engineers and stakeholders without relying on formal authority Are results-oriented and pragmatic, willing to do unglamorous work when it's the highest-leverage thing Are excited about learning the fundamentals of machine learning research (deep ML expertise is not required) Care about the societal impacts of your work Strong candidates may also have Experience with large-scale ETL and columnar or analytical storage (e.g., Spark, BigQuery, ClickHouse, DuckDB, Parquet) Experience with metrics or experiment-tracking systems, or high-volume time-series data Experience with dataset management, cataloging, or lineage tooling Built developer tooling or internal data platforms for demanding technical users — including in domains like quantitative trading, where fast-moving, exploratory data work looks a lot like research A working knowledge of machine learning Worked in, or closely with, an ML research lab Interest in — or experience with — people management and growing engineers The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $405,000 — $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role As a Full-Stack Software Engineer in RL, you'll build the platforms, tools, and interfaces that power environment creation, data collection, and training observability. The quality of Claude's next generation depends on the quality of the data we train it on — and the systems you build are what make that data possible. You'll own product surfaces end-to-end — from backend services and APIs to the web UIs that researchers, external vendors, and thousands of data labelers use every day. You don't need a background in ML research. What matters is that you can take an ambiguous, high-stakes problem and ship a polished, reliable product against it, fast. This team moves very quickly. Claude writes a lot of the code we commit, which means the bottleneck isn't typing — it's judgment, taste, and the ability to react to what researchers need next. You'll iterate on data collection strategies to distill the knowledge of thousands of human experts around the world into our models, and you'll do it in a loop that closes in hours and days, not quarters or months. Anthropic's Reinforcement Learning organization leads the research and development that trains Claude to be capable, reliable, and safe. We've contributed to every Claude model, with significant impact on the autonomy and coding capabilities of our most advanced models. Our work spans teaching models to use computers effectively, advancing code generation through RL, pioneering fundamental RL research for large language models, and building the scalable training methodologies behind our frontier production models. The RL org is organized around four goals: solving the science of long-horizon tasks and continual learning, scaling RL data and environments to be comprehensive and diverse, automating software engineering end-to-end, and training the frontier production model. Our engineering teams build the environments, evaluation systems, data pipelines, and tooling that make all of this possible — from realistic agentic training environments and scalable code data generation to human data collection platforms and production training operations. What You'll Do Build and extend web platforms for RL environment creation, management, and quality review — including environment configuration, versioning, and validation workflows Develop vendor-facing interfaces and tooling that let external partners create, submit, and iterate on training environments with minimal friction Design and implement platforms for human data collection at scale, including labeling workflows, quality assurance systems, and feedback mechanisms that surface reward signal integrity issues early Build evaluation dashboards and observability UIs that give researchers real-time insight into environment quality, training run health, and reward hacking Create backend services and APIs that connect environment authoring tools, data collection systems, and RL training infrastructure Build and expand scalable code data generation pipelines, producing diverse programming tasks with robust reward signals across languages and difficulty levels Develop onboarding automation and documentation tooling so new vendors and internal users ramp up in hours, not weeks Partner closely with RL researchers, data operations, and vendor management to translate ambiguous requirements into well-scoped, well-designed products You May Be a Good Fit If You Have strong software engineering fundamentals and real full-stack range — you're comfortable owning a surface from database schema to frontend Are proficient in Python and a modern web stack (React, TypeScript, or similar) Have a track record of shipping systems that solved a hard problem , not just shipped on time — e.g. you built the thing that made your team 10x faster, or the internal tool nobody thought was possible Operate with high agency: you identify what needs to be done and drive it forward without waiting for a ticket Have found yourself wondering "why isn't this moving faster?" in previous roles — and then have done something about it Care about UX and can build interfaces that are intuitive for both technical researchers and non-technical labelers Communicate clearly with researchers, operations teams, and engineers, and can turn vague asks into well-scoped work Thrive in a fast-moving environment where priorities shift, Claude is your pair programmer, and the next problem is often one nobody has solved before Care about Anthropic's mission to build safe, beneficial AI and want your work to contribute directly to it Strong Candidates May Also Have Built data collection, labeling, or annotation platforms — ideally ones that had to scale across many vendors or many task types Background building multi-tenant platforms with role-based access, audit trails, and vendor management workflows Experience with cloud infrastructure (GCP or AWS), Docker, and CI/CD pipelines Familiarity with LLM training, fine-tuning, or evaluation workflows Experience with async Python (Trio, asyncio) or high-throughput API design Background in dashboards, monitoring, or observability tooling Experience working directly with external vendors or partners on technical integrations A background that isn't a straight line — e.g. math or physics into SWE, competitive programming, research into engineering, or a side project that outgrew its scope Representative Projects Building a unified platform for human data collection that integrates labeling workflows, vendor management, and QA for complex agentic tasks Developing vendor onboarding automation that handles Docker registry access, API token management, and environment validation Creating evaluation and observability dashboards that catch reward hacks, measure environment difficulty, and give real-time feedback during production training Building environment quality review workflows that let researchers browse, grade, and provide feedback on training environments Developing automated environment quality pipelines that validate correctness and difficulty calibration before environments hit production training Building internal tools for browsing and analyzing training run results, environment statistics, and data collection progress The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $300,000 — $405,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role: We're hiring several Product Operations Managers to embed within our core product teams. Each will contribute to a key component of our stack. Whether you are ensuring models work within each of our harnesses, prioritizing a great experience across cloud offerings, or building the application layer that keeps users on the frontier, the Product Operations team is an integral part of delivering great products to the world. Product Operations is the connective tissue between Research, Product, and the market. We shorten the distance between what our models can do and what people get to use. Each of us sits inside one product team as an extension of its leadership, and together we run one shared operating system for how Anthropic ships and learns. On a given day you might get a launch through its final review, run an early access window, or triaging customer feedback. The rest of the week you build the systems so the team ships and learns faster next time. Our team is small and gets unusual leverage from Claude enabling us to fill gaps across the company and deliver outsized value. Responsibilities: 1. Ship with confidence Own launch readiness for your team. Keep our launch calendar the source of truth for what is shipping and when. Get every major launch through go/no-go: pre-read written, Security, Safeguards, Legal, and Commercial sign-offs recorded, and any accepted risk written down on purpose. Run early access and beta programs that return signal Product and Research can act on. Define the question before the window opens, give testers the scaffolding to answer it, and close each one with a retro on whether the feedback landed. Program manage new model launches on your surface: readiness goals set in advance, testing and eval coverage tracked, issues triaged, prompting changes landed, retro run. You won't write the evals. You run the program around them. 2. Close the loop with users Turn a lot of noise into two clear signals: what users are asking for, ranked by impact and mapped to the roadmap, and which customers or cohorts are blocked, on what, and why it matters. Plug your team into Anthropic's shared feedback systems rather than building a private one, and improve the shared systems where they fall short. Make sure the field, Support, and Research hear back about what happened with what they sent. 3. Run the team's operating system Set the team's pace: planning, business and execution reviews, goal tracking, offsites. Own how your team works with Legal, Safeguards, Security, GTM, and other product teams so partnering with your team is predictable. Push reporting overhead toward zero. Reviews and digests should be generated from data that already exists. Fill the gaps. Every embedded seat carries one or two projects unique to its team, for example managing Monthly Business Reviews and helping track progress between cycles. 4. Build with Claude Build the tools you need yourself: Claude-powered triage, dashboards that read and write to our systems of record, skills and agents that take the chasing out of the job. Contribute what you build back to the ProdOps team as reusable patterns (launch checklists, EAP setup, lookbacks, deprecations) so nobody starts from scratch. You may be a good fit if you have: Have owned an operational program end to end (launches, early access, feedback, or planning) that a product team relied on to make decisions. You designed it and ran it, not just executed someone else's plan. Have built a process from zero to one and then simplified it as it scaled. You treat process as a product: it has users, metrics, and a happy path people want to follow. Have worked embedded with product leadership and can move a team without formal authority. Build with AI yourself. You have shipped Claude or other LLM-powered workflows, written the prompts, iterated on the outputs, and can describe model behavior with specifics. Have direct experience managing evals, refining system prompts, and adapting harnesses to new models. Have direct experience working with Cloud providers in partnership or GTM capacity Have commitment to safe deployment of AI. Bonus if experience with AI Safety and Safeguards. Bonus: Program managed model launches or model quality at an application-layer AI product. Run early access, beta, or design partner programs. Product management, technical program management, chief of staff, or strategy and operations experience at a startup or fast-growing product company. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $240,000 — $260,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic’s Product Engineering org is looking for experienced engineers with strong full-stack fundamentals to join one of teams owning our current or emergent products. You'll be a technical leader who thinks holistically about the consumer and/or enterprise end user experience - be that via Claude.ai , the Anthropic API, enterprise deployments, Claude Code, or mission-driven applications - and carry real end-to-end ownership. You'll partner with engineering managers, product leaders, designers, and researchers to understand new model capabilities and redefine what is possible for users in the world of LLMs - and how to build it. We’ll look to you to have a product-oriented mindset and input, own technical quality across the stack (performance, accessibility, reliability, and developer experience), scale your efforts to millions of users on a global scale, and carry genuine excitement about what AI makes possible. We have multiple teams that are currently hiring. Team placement occurs after the interview process, taking into account your interests and experience alongside organizational needs. This flexible approach allows us to match talented engineers with the backend product efforts where they'll have the greatest impact and growth potential. What you'll do: Platform Ecosystem: The Ecosystem team is empowering the next big industries beyond coding to build on our platform & creating the right commercial rails to let any business in our ecosystem profit from building on Claude. As an early member of this foundational effort, you'd identify and build for the next 10x markets starting with Life Sciences and Healthcare. We also build the commercial rails and controls that let the platform do business anywhere, with anyone, and turn Claude from a cost line into a revenue line. Beneficial Deployments: Beneficial Deployments Engineering brings Claude to organizations doing the most good with the fewest resources — nonprofits, schools, healthcare providers, researchers, and economic mobility programs.We're looking for a full-stack engineer to build the access programs, tooling, and product work that make frontier AI usable for teams that couldn't afford this capacity any other way. Vertical AI Products: Purpose-built experiences for specific industries where Claude can transform complex professional work. We're currently building for three verticals, with more to come: Life Sciences — an agentic research platform for scientists: specialist agents for computational biology, literature review, and regulatory review, built on model capabilities we're investing in for biology and chemistry. Live with early customers and expanding fast. Enterprise AI Products: You'll work on the products that make Claude a daily-use tool for enterprise customers across industries – the connective tissue that lets Claude operate effectively across workflows. On this team you will systematically understand why Enterprise users aren't activating, what's blocking adoption, and building the capabilities to close those gaps.Some of our focuses are: Extensibility — plugins, skills, connectors, and the MCP ecosystem that lets a company shape Claude around their specific workflows and distribute that work across teams. Context — enterprise knowledge, organizational memory, and the retrieval layer that makes Claude genuinely aware of a company's people, documents, and workflows. A connected, contextual Claude is the difference between a general-purpose chatbot and a real coworker. Proactivity — Cowork suggestions, workflow capture, ambient Claude inside the apps people already use. Moving from "Claude answers when asked" to "Claude notices what you need". Public Sector: Build products that deliver Claude to the U.S. federal & state governments and allied democracies — from FedRAMP environments to classified networks. We're a startup-minded team with huge surface area: we own Claude for Government, ship zero-to-one products into the most regulated environments in the world, and work directly with the agencies using them Enterprise Foundations: You'll build the systems large organizations require before they can adopt Claude at scale: identity and permissions, security and compliance controls, and the admin analytics that let them see how it's being used. This is the work that turns "we love the demo" into a signed enterprise deal.The role is part product, part platform. You'll work closely with Product and GTM to understand what our largest customers need, then build it once in a way that works across Claude.ai, Claude Code, and Cowork. Growth: Drive user acquisition, engagement, retention, and monetization through data-driven strategies and technical implementations. At Anthropic, we're not just building AI tools; we're reimagining how AI can enhance and expand its user base! As a member of the growth team, you will have a unique opportunity to shape our growth strategy. You will work with a cross-functional team of engineers, data scientists, marketers, and product managers to design, implement, and optimize growth initiatives that scale our AI-powered tools and maximize their impact. You might be a good fit if you: Have a minimum of 8 years of practical full-stack engineering experience, ideally with 2+ years operating at a Staff or equivalent technical leadership level Have led the design and delivery of complex, consumer or B2B user-facing products across the full stack Are a technical expert in modern frontend and backend development, with demonstrated depth in key languages and frameworks (ie. React, Typescript) Take a product-focused approach to building solutions that are robust, scalable, and easy to use Have worked in early start-up or otherwise fast moving, rapidly evolving environments, and have ideally built products from 0 to 1 Care deeply in investing in the mentorship and growth of your peers Have successfully driven cross-team/cross-org organizational alignment to get impactful work shipped, and work with influence over authority Have experience establishing engineering standards, component architectures, and development best practices Thrive in fast-paced environments and can navigate ambiguity to deliver high-quality products Deadline to apply: None. Applications will be reviewed on a rolling basis. Location Preference: Preference will be given to candidates based in NY, NJ, SEA, SF or the Bay Area given the current location of team. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $405,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.